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Prior art has shown it is possible to estimate, through image processing andcomputer vision techniques, the types and parameters of transformations thathave been applied to the content of individual images to obtain new images.Given a large corpus of images and a query image, an interesting further stepis to retrieve the set of original images whose content is present in the queryimage, as well as the detailed sequences of transformations that yield thequery image given the original images. This is a problem that recently hasreceived the name of image provenance analysis. In these times of public mediamanipulation ( e.g., fake news and meme sharing), obtaining the history ofimage transformations is relevant for fact checking and authorshipverification, among many other applications. This article presents anend-to-end processing pipeline for image provenance analysis, which works atreal-world scale. It employs a cutting-edge image filtering solution that iscustom-tailored for the problem at hand, as well as novel techniques forobtaining the provenance graph that expresses how the images, as nodes, areancestrally connected. A comprehensive set of experiments for each stage of thepipeline is provided, comparing the proposed solution with state-of-the-artresults, employing previously published datasets. In addition, this workintroduces a new dataset of real-world provenance cases from the social mediasite Reddit, along with baseline results.

Image Provenance Analysis at Scale